
Matching-adjusted indirect comparison (MAIC). The trial assignment model in Equation REF  contains main effect terms for all three effect modifiers — only covariate means are balanced. The objective function in Equation REF  is minimized using BFGS [1]}. The weights estimated by Equation REF  are used to fit a weighted simple linear regression of outcome on treatment to the index trial IPD.

Two-stage matching-adjusted indirect comparison (2SMAIC). We follow the same steps as for the standard MAIC. In addition, the treatment assignment model in Equation REF  is fitted to the index study IPD, including main effect terms for all three baseline covariates. Propensity score estimates are generated by Equation REF  and combined with the weights generated by Equation REF  as per Equation REF . The resulting weights are used to fit a weighted simple linear regression of outcome on treatment to the index trial IPD.

Truncated matching-adjusted indirect comparison (T-MAIC). This approach is identical to MAIC but the highest estimated weights (Equation REF ) are truncated using a 95th percentile cutpoint, following Susukida et al. [2]}, [3]} and Lee et al. [4]}. Specifically, all weights above the 95th percentile are replaced by the value of the 95th percentile.

Truncated two-stage matching-adjusted indirect comparison (T-2SMAIC). This approach is identical to 2SMAIC but all the estimated weights (Equation REF ) larger than the 95th percentile are set equal to the 95th percentile.

